349 research outputs found
Safe Learning of Quadrotor Dynamics Using Barrier Certificates
To effectively control complex dynamical systems, accurate nonlinear models
are typically needed. However, these models are not always known. In this
paper, we present a data-driven approach based on Gaussian processes that
learns models of quadrotors operating in partially unknown environments. What
makes this challenging is that if the learning process is not carefully
controlled, the system will go unstable, i.e., the quadcopter will crash. To
this end, barrier certificates are employed for safe learning. The barrier
certificates establish a non-conservative forward invariant safe region, in
which high probability safety guarantees are provided based on the statistics
of the Gaussian Process. A learning controller is designed to efficiently
explore those uncertain states and expand the barrier certified safe region
based on an adaptive sampling scheme. In addition, a recursive Gaussian Process
prediction method is developed to learn the complex quadrotor dynamics in
real-time. Simulation results are provided to demonstrate the effectiveness of
the proposed approach.Comment: Submitted to ICRA 2018, 8 page
Accurate Tracking of Aggressive Quadrotor Trajectories using Incremental Nonlinear Dynamic Inversion and Differential Flatness
Autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e.,
high-speed and high-acceleration) maneuvers have attracted significant
attention in the past few years. This paper focuses on accurate tracking of
aggressive quadcopter trajectories. We propose a novel control law for tracking
of position and yaw angle and their derivatives of up to fourth order,
specifically, velocity, acceleration, jerk, and snap along with yaw rate and
yaw acceleration. Jerk and snap are tracked using feedforward inputs for
angular rate and angular acceleration based on the differential flatness of the
quadcopter dynamics. Snap tracking requires direct control of body torque,
which we achieve using closed-loop motor speed control based on measurements
from optical encoders attached to the motors. The controller utilizes
incremental nonlinear dynamic inversion (INDI) for robust tracking of linear
and angular accelerations despite external disturbances, such as aerodynamic
drag forces. Hence, prior modeling of aerodynamic effects is not required. We
rigorously analyze the proposed control law through response analysis, and we
demonstrate it in experiments. The controller enables a quadcopter UAV to track
complex 3D trajectories, reaching speeds up to 12.9 m/s and accelerations up to
2.1g, while keeping the root-mean-square tracking error down to 6.6 cm, in a
flight volume that is roughly 18 m by 7 m and 3 m tall. We also demonstrate the
robustness of the controller by attaching a drag plate to the UAV in flight
tests and by pulling on the UAV with a rope during hover.Comment: To be published in IEEE Transactions on Control Systems Technology.
Revision: new set of experiments at increased speed (up to 12.9 m/s), updated
controller design using quaternion representation, new video available at
https://youtu.be/K15lNBAKDC
Perception-aware time optimal path parameterization for quadrotors
The increasing popularity of quadrotors has given rise to a class of
predominantly vision-driven vehicles. This paper addresses the problem of
perception-aware time optimal path parametrization for quadrotors. Although
many different choices of perceptual modalities are available, the low weight
and power budgets of quadrotor systems makes a camera ideal for on-board
navigation and estimation algorithms. However, this does come with a set of
challenges. The limited field of view of the camera can restrict the visibility
of salient regions in the environment, which dictates the necessity to consider
perception and planning jointly. The main contribution of this paper is an
efficient time optimal path parametrization algorithm for quadrotors with
limited field of view constraints. We show in a simulation study that a
state-of-the-art controller can track planned trajectories, and we validate the
proposed algorithm on a quadrotor platform in experiments.Comment: Accepted to appear at ICRA 202
A Comparative Study of Nonlinear MPC and Differential-Flatness-Based Control for Quadrotor Agile Flight
Accurate trajectory-tracking control for quadrotors is essential for safe navigation in cluttered environments. However, this is challenging in agile flights due to nonlinear dynamics, complex aerodynamic effects, and actuation constraints. In this article, we empirically compare two state-of-the-art control frameworks: the nonlinear-model-predictive controller (NMPC) and the differential-flatness-based controller (DFBC), by tracking a wide variety of agile trajectories at speeds up to 20 m/s (i.e., 72 km/h). The comparisons are performed in both simulation and real-world environments to systematically evaluate both methods from the aspect of tracking accuracy, robustness, and computational efficiency. We show the superiority of the NMPC in tracking dynamically infeasible trajectories, at the cost of higher computation time and risk of numerical convergence issues. For both methods, we also quantitatively study the effect of adding an inner loop controller using the incremental nonlinear dynamic inversion method, and the effect of adding an aerodynamic drag model. Our real-world experiments, performed in one of the world’s largest motion capture systems, demonstrate more than 78% tracking error reduction of both NMPC and DFBC, indicating the necessity of using an inner loop controller and aerodynamic drag model for agile trajectory tracking
DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control
Precise arbitrary trajectory tracking for quadrotors is challenging due to
unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To
tackle these challenges, we present Deep Adaptive Trajectory Tracking (DATT), a
learning-based approach that can precisely track arbitrary, potentially
infeasible trajectories in the presence of large disturbances in the real
world. DATT builds on a novel feedforward-feedback-adaptive control structure
trained in simulation using reinforcement learning. When deployed on real
hardware, DATT is augmented with a disturbance estimator using L1 adaptive
control in closed-loop, without any fine-tuning. DATT significantly outperforms
competitive adaptive nonlinear and model predictive controllers for both
feasible smooth and infeasible trajectories in unsteady wind fields, including
challenging scenarios where baselines completely fail. Moreover, DATT can
efficiently run online with an inference time less than 3.2 ms, less than 1/4
of the adaptive nonlinear model predictive control baselin
Online optimisation-based backstepping control design with application to quadrotor
In backstepping implementation, the derivatives of virtual control signals are required at each step. This study provides a novel way to solve this problem by combining
online optimisation with backstepping design in an outer and inner loop manner. The
properties of differential flatness and the B-spline polynomial function are exploited
to transform the optimal control problem into a computationally efficient form. The
optimisation process generates not only the optimised states but also their finite order
derivatives which can be used to analytically calculate the derivatives of virtual control signal required in backstepping design. In addition, the online optimisation repeatedly performed in a receding horizon fashion can also realise local motion planning for obstacle avoidance. The stability of the receding horizon control scheme is analysed via
Lyapunov method which is guaranteed by adding a parametrised terminal condition in the online optimisation. Numerical simulations and flight experiments of a quadrotor unmanned air vehicle are given to demonstrate the effectiveness of the proposed composite control method
Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement
In this paper, we present a novel solution for real-time, Non-Linear Model
Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The
proposed controller formulates the Optimal Control Problem (OCP) in terms of
flat outputs over an adaptive lattice. In common approximated OCP solutions,
the number of discretization points composing the lattice represents a critical
upper bound for real-time applications. The proposed NMPC-based technique
refines the initially uniform time horizon by adding time steps with a sampling
criterion that aims to reduce the discretization error. This enables a higher
accuracy in the initial part of the receding horizon, which is more relevant to
NMPC, while keeping bounded the number of discretization points. By combining
this feature with an efficient Least Square formulation, our solver is also
extremely time-efficient, generating trajectories of multiple seconds within
only a few milliseconds. The performance of the proposed approach has been
validated in a high fidelity simulation environment, by using an UAV platform.
We also released our implementation as open source C++ code.Comment: In: 2018 IEEE International Conference on Simulation, Modeling, and
Programming for Autonomous Robots (SIMPAR 2018
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